Senior Data Scientist - Credit Risk

Job Description As a Senior Data Scientist at our company, you will play a crucial role in building a cutting-edge Credit Risk Machine Learning platform. Your responsibilities will include designing, developing, and deploying machine learning models that assess credit risk with high accuracy, interpretability, and regulatory compliance. Your expertise in machine learning credit risk modeling and strong Python programming skills will drive innovation and deliver business value to our clients. Key Responsibilities: - Build and develop credit risk models to assess credit risk, ensuring high accuracy and explainability to comply with regulatory frameworks. - Lead the development of a machine learning platform that allows clients to customize their own credit risk models for tailored risk management solutions. - Align data solutions with business objectives and technical constraints, adapting to market dynamics, regulatory requirements, and client needs. - Collaborate with cross-functional teams to manage and lead various aspects of the product lifecycle from conception to delivery. - Monitor model performance, maintain risk management protocols by retraining models as needed, and ensure ongoing accuracy and regulatory compliance. - Stay updated on industry trends, regulations, and emerging technologies to continuously improve models and provide mentorship to junior team members. Qualifications Required: Technical Skills: - In-depth understanding of machine learning algorithms (supervised, unsupervised, ensemble methods) and their application to credit risk. - Expertise in statistical analysis, including hypothesis testing, regression analysis, probability theory, and data modeling techniques. - Experience in designing, developing, and delivering end-to-end data products and solutions. - Proficiency in model explainability techniques (e.g., SHAP, LIME) and regulatory compliance for risk models. - Strong proficiency in Python and working knowledge of PySpark. - Ability to build and deploy models on cloud platforms (e.g., AWS). - Experience with NLP techniques is a plus. Domain Skills: - Ability to collaborate with finance and risk teams to ensure model outputs align with business objectives and regulatory requirements. - Understanding of credit risk management processes, credit scoring, default probability estimation, and financial concepts. - Knowledge of finance and credit risk concepts, regulatory frameworks (e.g., Basel III, IFRS 9), and their impact on risk assessment models. Education and Experience: - Bachelor's/Advanced degree in Data Science, Statistics, Mathematics, Computer Science, or related field. - 3 to 5 years of experience in data science and machine learning domain. - Experience in the financial sector or credit risk management is a bonus. As a Senior Data Scientist at our company, you will play a crucial role in building a cutting-edge Credit Risk Machine Learning platform. Your responsibilities will include designing, developing, and deploying machine learning models that assess credit risk with high accuracy, interpretability, and regulatory compliance. Your expertise in machine learning credit risk modeling and strong Python programming skills will drive innovation and deliver business value to our clients. Key Responsibilities: - Build and develop credit risk models to assess credit risk, ensuring high accuracy and explainability to comply with regulatory frameworks. - Lead the development of a machine learning platform that allows clients to customize their own credit risk models for tailored risk management solutions. - Align data solutions with business objectives and technical constraints, adapting to market dynamics, regulatory requirements, and client needs. - Collaborate with cross-functional teams to manage and lead various aspects of the product lifecycle from conception to delivery. - Monitor model performance, maintain risk management protocols by retraining models as needed, and ensure ongoing accuracy and regulatory compliance. - Stay updated on industry trends, regulations, and emerging technologies to continuously improve models and provide mentorship to junior team members. Qualifications Required: Technical Skills: - In-depth understanding of machine learning algorithms (supervised, unsupervised, ensemble methods) and their application to credit risk. - Expertise in statistical analysis, including hypothesis testing, regression analysis, probability theory, and data modeling techniques. - Experience in designing, developing, and delivering end-to-end data products and solutions. - Proficiency in model explainability techniques (e.g., SHAP, LIME) and regulatory compliance for risk models. - Strong proficiency in Python and working knowledge of PySpark. - Ability to build and deploy models on cloud platforms (e.g., AWS). - Experience with NLP techniques is a plus. Domain Skills: - Ability to collaborate with finance and risk teams to ensure model outputs align with bus